Bacteria–surface interactions: role of impacting bacteria-laden droplets
Bibliographic record
Abstract
as the model microbe, we investigate the impact of a bacteria-laden droplet on different substrates, both bare and antimicrobial. In doing so, we unveil the significance of kinetic energy and spreading parameters of the impacting droplet in determining the microbes' proliferation capabilities. Our results indicate an inverse relationship between the impact Weber number and the bacterial ability to proliferate. We reveal that the mechanical stress generated during impact impedes the capabilities of microbes present inside the droplet to create their progeny. Following an order analysis of the mechanical stress generated, we argue that the impact does not induce lysis-driven cell death of the bacteria; rather, it promotes a stress-driven transition of viable bacteria to a viable-but-non-culturable (VBNC) state. Furthermore, variations in the concentration of particles on the antimicrobial surfaces revealed the role of the post-impact spreading behaviour in dictating bacterial proliferation capabilities. Contrary to the conventional notion, we demonstrate that during the early stages of interaction, a bare substrate may outperform an antibacterial substrate in the inactivation of the bacterial load. Finally, we present an interaction map illustrating the complex relationship between bacterial colony-forming units, bactericide concentration on the surface, and the impact Weber number. We believe that the inferences of the study, highlighting the effect of mechanical stresses on the soft cell wall of microbes, could be a useful design consideration for the development of antimicrobial surfaces.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".